Peptide-Mediated Growth and Dispersion of Au Nanoparticles in Water

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C: Physical Processes in Nanomaterials and Nanostructures

Peptide-Mediated Growth and Dispersion of Au Nanoparticles in Water via Sequence Engineering Michelle A. Nguyen, Zak E. Hughes, Yang Liu, Yue Li, Mark T. Swihart, Marc R. Knecht, and Tiffany R. Walsh J. Phys. Chem. C, Just Accepted Manuscript • DOI: 10.1021/acs.jpcc.8b02392 • Publication Date (Web): 03 May 2018 Downloaded from http://pubs.acs.org on May 4, 2018

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The Journal of Physical Chemistry

Peptide-Mediated Growth and Dispersion of Au Nanoparticles in Water via Sequence Engineering Michelle A. Nguyen,1,# Zak E. Hughes,2,# Yang Liu,3 Yue Li,3 Mark T. Swihart,3 Marc R. Knecht,1,* and Tiffany R. Walsh2,* 1

Department of Chemistry, University of Miami, 1301 Memorial Drive, Coral Gables, Florida

33146, United States 2

Institute for Frontier Materials, Deakin University, Geelong, Victoria 3216, Australia

3

Department of Chemical and Biological Engineering, University at Buffalo (SUNY), Buffalo,

New York 14260, United States (#: These authors contributed equally)

RECEIVED DATE (to be automatically inserted after your manuscript is accepted if required according to the journal that you are submitting your paper to) *To whom correspondence should be addressed: MRK: Phone: (305) 284-9351, email: [email protected]; TRW: Phone: +61 (35) 227-3116, email: [email protected]

ACS Paragon Plus Environment

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Abstract

The use of peptides to nucleate, grow, and stabilize nanoparticles in aqueous media via noncovalent interactions offers new possibilities for creating functional, water-dispersed inorganic/organic hybrid materials, particularly for Au nanoparticles. Numerous previous studies have identified peptide sequences that both possess a strong binding affinity for Au surfaces and are capable of supporting nanoparticle growth in water. However, recent studies have shown that not all such peptide sequences can produce stable dispersions of these nanoparticles. Here, via integrated experiments and molecular modeling, we provide new insights into the many factors that influence Au nanoparticle growth and stabilization in aqueous media. We define colloidal stability by the absence of visible precipitation after at least 24 hours post-synthesis. We use binding affinity measurements, nanoparticle synthesis, characterization and stabilization assays, and molecular modeling, to investigate a set of sequences based on two known peptides with strong affinity for Au. This set of biomolecules is designed to probe specific sequence and context effects using both point mutations and global reorganization of the peptides. Our data confirm, for a broader range of sequences, that Au nanoparticle/peptide binding affinity alone is not predictive of peptide-mediated colloidal stability. By comparing nanoparticle stabilization assay outcomes with molecular simulations, we establish a correlation between the colloidal stability of the Au nanoparticles and the degree of conformational diversity in the surfaceadsorbed peptides. Our findings suggest future routes to engineer peptide sequences for biobased growth and dispersion of functional nanoparticles in aqueous media.


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Introduction Bio-inspired peptide-mediated strategies are an effective approach to nucleate, grow, stabilize, activate, and organize inorganic nanoparticles in aqueous media.1 Despite these successes, broader applicability of such strategies is limited by a lack of fundamental understanding of how to manipulate the surface-adsorbed structure, and consequently the binding strength and concomitant properties, of materials-binding peptides. Previous studies2-4 have demonstrated that a peptide sequence with strong materials-binding affinity does not necessarily also serve as an effective agent for stabilizing a dispersion of peptide-capped nanoparticles. The reasons for this disconnect are currently unknown. This drives the need to identify the features of peptide sequences that can promote strong materials-binding affinity, effective nanoparticle stabilization capability, or both. Alteration of peptide sequences (e.g. via point mutations or by sequence scrambling) has been used in prior investigations of peptide binding affinity and nanoparticle stabilization.3,

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However, detailed molecular-level structural data that would enable clear conceptual links between a given peptide sequence, its bound conformation(s) on a surface, and the peptide’s ability to cap and stabilize inorganic nanoparticles are still far from comprehensive, and remain much needed for advancing our understanding of this problem. For example, a point mutation can eliminate surface binding at that particular point in the sequence, but it might also lead to large-scale conformational changes throughout the rest of the peptide when it is adsorbed onto the particle surface. This lack of a predictable response to mutations severely hinders clear interpretation of experiments that seek to identify which residues are the most critical for mediating surface binding to nanomaterials. As a critical first step to addressing this challenging problem, here we aim to progress the systematic construction of a structure/property knowledge-base for peptide-stabilized


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nanomaterials. To do this, we build on two concepts that were introduced in prior work. The first is the concept of anchor residues.6 We define anchor residues to be those residues that are known to interact strongly with gold (from predictions of amino acid binding strengths7), namely Trp, Tyr, Arg, Met, and to some extent Phe and His, and that support a strong degree of residue-metal surface contact of 80% or greater. Our definition makes a distinction between residues that make strong contact with the surface due to specific interactions, as opposed to residues that can coincidentally be found close to the surface but are not considered strong binders, such as Ala (based on amino acid binding data7). A second concept used throughout the present study is conformational susceptibility, which we introduced in earlier studies3 to interpret the structural outcomes of point mutations in materials-binding peptides. Briefly, a sequence is conformationally susceptible if point mutations of an anchor residue to Ala resulted in a distinct difference in the conformational ensemble of the peptide. We consider a conformationally recalcitrant peptide to be a sequence that features minimal structural change following such point mutations. In this work we have quantified and elucidated how the mutations of two different parent peptide sequences with very different conformational susceptibilities can influence both the noncovalent binding affinity at aqueous Au interfaces, and the ability of these biomolecules to nucleate, grow, and stabilize Au nanoparticles (NPs) in aqueous media. Specifically, we provide an in depth analysis and characterization of the Au-binding behavior of mutants of two wellknown Au-binding sequences: A3 (AYSSGAPPMPPF) and AuBP1 (WAGAKRLVLRRE). Our mutation studies provide new insights into the sequence characteristics that control peptidematerials binding affinity, and can explain how and why particular mutants of these sequences can stabilize Au NPs in solution. Following previous work,3 we considered five sequence


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variations each for A3 and AuBP1, as summarized in Table 1. For the two parent peptides, we replaced each known anchor residue (identified from our previous work6) with alanine (sequences A3-1 to A3-3, and AuBP1-1 to AuBP1-3). We also considered a randomized version of each peptide sequence (A3-4 and AuBP1-4) and a variation of the peptide sequence in which all anchor residues were grouped together at the N-terminus (A3-5 and AuBP1-5). Including the original parent biomolecules, this gives a total of twelve unique peptide sequences. Through a combination of quartz crystal microbalance (QCM) measurements, NP synthesis and characterization, and advanced molecular dynamics (MD) simulations, we have quantified and evaluated how each of these twelve sequences adsorbs to Au surfaces and the ability of each of these peptides to support the nucleation, growth, and stabilization of Au NPs in aqueous media. Our results provide key insights into the effects of local sequence context (i.e. the impact of the neighboring residues surrounding anchor residues) on the binding of peptides to their target inorganic surface, providing a pathway toward the de novo design of new sequences with enhanced binding affinity and NP stabilization capability.

Materials and Methods Chemicals. Ammonium hydroxide (20%) and hydrogen peroxide (30%) were purchased from BDH Chemicals. HAuCl4 and NaBH4 were obtained from Sigma-Aldrich. The peptides used were acquired from Genscript at >90% purity. All chemicals employed in this study were used as received without further purification. Milli-Q water (18 MΩ⋅cm) was used for all experiments. QCM Binding Experiments. All QCM measurements, including those for dissipation energy, were conducted using a Q-Sense E4 instrument (Biolin Scientific) following


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established methods.6,

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Standard polycrystalline Au crystal sensors were cleaned

according to manufacturer protocols before use. Briefly, the Au QCM sensors were subjected to UV-ozone exposure, followed by immersion in a 5:1:1 (v/v/v) water/ammonium hydroxide/hydrogen peroxide solution and an additional UV/ozone treatment. An aqueous peptide solution at a concentration of 2.5 to 15 µg/mL was flowed over the sensor surface at a rate of 150 µL/min. The frequency change and dissipation energy were recorded for 30 min to ensure saturation. The frequency change is directly related, via the Sauerbrey equation, to the mass of peptide adsorbed, from which rate parameters for binding can be determined.6, 9 All QCM experiments were performed at 22.5 °C, and the pH of the peptide solutions ranged from 4.1 – 6.0 depending on the sequence. Peptide solutions were prepared by dissolving the peptide in deionized water at the appropriate concentration. Au nanoparticle synthesis. Peptide-capped Au NPs were prepared according to synthetic methods established by Li et al.10 In a typical experiment, 500 µL of an aqueous 1.0 mM peptide solution was added to 4.460 mL of water in a vial. Next, 10 µL of an aqueous 100 mM HAuCl4 solution was added. The solution was briefly agitated and allowed to stand for 10 min before adding 30 µL of a freshly prepared aqueous 100 mM NaBH4 solution. A NaBH4:Au ratio of 3 was used for all syntheses. The solution was briefly agitated, and the reaction was allowed to proceed undisturbed at room temperature for 1.0 h to ensure complete reduction. Nanoparticle characterization. Once fabricated, the NPs were optically characterized using a Shimadzu 3600 UV-vis-NIR spectrophotometer with a 1 cm path length quartz cuvette. The size and shape of the Au NPs were characterized using a JEOL JEM-2010


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TEM operating at a working voltage of 200 kV. Samples were prepared for imaging by drop-casting 5 to 15 µL of the NP dispersion onto a carbon-coated Cu TEM grid. NP size distributions were determined by measuring >100 individual NPs from TEM images of each sample using the Nano Measurer 1.2 image analysis software. Within this software, the boundaries of each NP were located manually. Size distribution histograms are available in the Supporting Information, Figure S1. Molecular Simulations. The surface-adsorbed conformational ensembles of the A3- and AuBP1-based sequences were predicted using Replica Exchange with Solute Tempering (REST)11-12 molecular dynamics (MD) simulations. We performed a REST-MD simulation for each of the twelve peptide sequences listed in Table 1. Each REST-MD simulation comprised a single peptide chain adsorbed at the Au(111) interface in the presence of liquid water. Earlier studies indicated that the Au(111) surface is a reasonable surrogate for the polycrystalline Au substrate, as used in the QCM experiments.3,

13-14

Three-dimensional periodic boundary

conditions were used throughout. The polarizable GolP-CHARMM force-field,15-16 the CHARMM22* force-field,17-18 and the modified TIP3P potential19-20 were used to describe the interactions involving the Au surface, the peptide, and water, respectively. The GolP-CHARMM force-field has been recently demonstrated to be in good agreement in the near-reproduction of the experimentally-determined binding free energy of the AuBP1 peptide at the aqueous Au interface.13 We used the Gromacs21 software package for all of the simulations described herein. All REST-MD simulations were carried out at a thermal temperature of 300 K, using 16 replicas to span “effective temperature” space.11 Full details regarding the simulation procedures and analysis of the simulation trajectories are provided in the ‘Additional Methodology’ section of the Supporting Information.


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Results and Discussion QCM Analysis of Peptide Binding to Au. Although the A3 and AuBP1 parent sequences are both Au-binding peptides22-24 and each possesses three anchor residues for Au-binding,6-7 AuBP1 is a substantially stronger binder than A3.6 To analyze the sequence characteristics that affect the kinetics and thermodynamics of binding, a set of five mutant peptide sequences was prepared for each parent biomolecule. QCM measurements quantified the free energy of binding (∆G) of each peptide of the library for Au surfaces, as shown in Table 1. As an example of the QCM analysis, Figure 1a presents the observed frequency changes for A3-1 at five different peptide concentrations. Higher peptide concentrations yielded faster and larger frequency changes, reflecting increased binding rates and increased amounts of peptide bound to the metallic surface. The dissipation energy, which is indicative of the viscoelasticity of the bound peptide layer, is shown for the highest peptide concentration (15 µg/mL). For all of the peptides studied here, the dissipation energy was observed to be ܵ௖௢௡௙ have a strong ௟௢௪ likelihood to cause Au NP precipitation, while those peptides with Sconf < ܵ௖௢௡௙ are likely to ௛௜௚௛

stabilize Au NP dispersion in aqueous media. We identified the range of values of ܵ௖௢௡௙ and ௟௢௪ ܵ௖௢௡௙ based on our previous work.3,

5-7, 33

௛௜௚௛

௟௢௪ The range of values between ܵ௖௢௡௙ and ܵ௖௢௡௙

represents an uncertainty band for which we suggest that the outcomes cannot be predicted with confidence. Figure 8 also highlights the four systems with the most inconsistency in terms of the NP stabilization assay, where the degree of confidence in our predictions is low. Our proposed classification based on Sconf values appears broadly consistent with the experimental data, in the sense that NPs that showed reasonably consistent stabilization behaviors (i.e. that were consistently stable or consistently unstable) were captured by our analysis. However, our metric was unable to capture inconsistent NP stabilization scenarios. We


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note here that our Sconf metric is an approximation based on single-chain adsorption properties (i.e. corresponding to dilute conditions), and therefore it is not surprising that it cannot cover all possible situations. For example, as suggested earlier, in some instances surface-driven interpeptide interactions are likely to influence stabilization. For the AuBP1 sequences in particular, inter-chain interactions in the surface-adsorbed state of the peptides may lead to deviations from our idealized conditions. To elaborate, unlike the Pd4 or A3 sequences, AuBP1 features several charged residues in solution at pH≈7, comprising a positively-charged Lys and three Arg residues, and a negatively-charged Asp. Evidence drawn from previous studies of multi-chain NP-adsorbed AuBP1 peptides5 suggests that inter-chain interactions involving oppositelycharged residues could be substantial. Further study is required to resolve the possible origins of the inconsistent stabilization properties for the four systems identified here. While pH-based mechanisms

offer

a

plausible

hypothesis,

several

mechanisms

governing

Au

NP

dispersion/precipitation are likely to co-exist, and remain to be proposed and tested in future work.

Conclusions By combining peptide-surface binding affinity measurements, nanoparticle synthesis, characterization and stabilization assays, and advanced molecular simulations, we have identified new factors that influence both Au nanoparticle growth and nanoparticle colloidal stability in aqueous media. Colloidal stability is defined here as the absence of visible precipitation after at least 24 hours post-synthesis. We based our investigation on two peptides with strong affinity for Au, considering both point mutations and re-ordering of these sequences. Our findings demonstrate that a strong Au nanoparticle peptide binding affinity does not always guarantee


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peptide-mediated colloidal stability. Instead, by comparing nanoparticle stabilization assay outcomes with molecular simulations, we established a metric that is broadly indicative of nanoparticle colloidal stability, based on the degree of conformational diversity in the surfaceadsorbed peptides. While our results offer guidance on the engineering of peptide sequences to realize peptide-mediated growth and dispersion of Au nanoparticles in aqueous media, alternative mechanisms governing colloidal stability are further required to comprehensively encompass the complex phenomena of peptide-based nanoparticle growth and stabilization.

Supporting Information: Additional methodological details, binding constants for the A3 and AuBP1 mutant peptides, QCM analysis, size distribution histograms, structural similarity analyses, and conformational analyses. This material is available free of charge via the Internet at http://pubs.acs.org.

Acknowledgments: This material is based upon work supported by the Air Office of Scientific Research, grant number FA9550-12-1-0226. We gratefully acknowledge the Victorian Life Sciences Computation Facility (VLSCI) for allocation of computational resources.

Notes: Current address for M.A.N is Department of Biomedical Engineering, University of Miami, FL, USA. Current address for Z.E.H is School of Chemistry and Biosciences, University of Bradford, Bradford, BD7 1DP, UK.


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Figure/Table Captions Table 1. Adsorption Analysis for A3 and AuBP1 Mutant Peptides on Aua Table 2. Degree of residue-surface contact (expressed as a percentage) for each of the six A3based peptide sequences, adsorbed at the aqueous Au interface, predicted from REST simulations. White, blue, green, orange, and red cell colors indicate 0-19%, 20-39%, 40-59%, 60-79%, and 80+% contact, respectively. Table 3. Degree of residue-surface contact (expressed as a percentage) for each of the six AuBP1-based peptide sequences, adsorbed at the aqueous Au interface, predicted from REST simulations. White, blue, green, orange, and red cell colors indicate 0-19%, 20-39%, 40-59%, 60-79%, and 80+% contact, respectively. Figure 1. QCM analysis of peptide binding on Au to obtain ka and kd values. For this example, the A3-1 peptide was used. Part (a) shows the inverted frequency change vs. time plot for five concentrations, as well as the dissipation energy plot for the highest concentration studied. Part (b) shows kobs values vs. peptide concentration, obtained from the data in part (a) by fitting using Langmuir kinetics. Note that no error bars are presented as they are smaller than the points in the graph. The slope and y-intercept in part (b) provide the rate constants for adsorption and desorption, respectively. Figure 2. Most likely structures of the six indicated A3-based peptides adsorbed at the aqueous Au interface, predicted from the REST simulations. Water not shown for clarity. Figure 3. Most likely structures of the six indicated AuBP1-based peptides adsorbed at the aqueous Au interface, predicted from the REST simulations. Water not shown for clarity. Figure 4. UV-vis absorbance spectra of the Au NPs prepared using the (a) A3 peptide and its mutants and (b) AuBP1 peptide and its mutants. Insets show images of the corresponding colloidal dispersions, approximately 1 h after synthesis. Figure 5. Representative TEM images of Au NPs prepared using the A3 peptide and its mutants, as labelled. Figure 6. Representative conformation of A3-4 adsorbed at the aqueous Au interface, in the instance where F10 is not in surface contact. Solvent-exposed hydrophobic residues P2, A4, A7 and F10 are highlighted in green, while anchor residues Y1 and M11 are highlighted in red. Water not shown for clarity. Figure 7. Representative TEM images of Au NPs prepared using the AuBP1 peptide and its mutants, as labelled. Note the different magnification used for AuBP1-1 and AuBP1-3 to highlight the degree of aggregation in these systems. Figure 8. Conformational entropic contributions to the binding (Sconf) calculated from REST-MD simulations. Green and red indicate stable and unstable Au NP dispersions as observed from experiment. Beige bars indicate systems that exhibited the most inconsistency in the nanoparticle


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௛௜௚௛

௟௢௪ and ܵ௖௢௡௙ correspond with values 3.10 and 3.28 respectively. stabilization experiments. ܵ௖௢௡௙ Pd4 data were taken from Ref 3.


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a

From QCM experiments: adsorption Gibbs free energy (∆G) values are given as mean ± one standard deviation from at least three independent experiments.

Table 1


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Table 2.


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Table 3.


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Figure 1.


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Figure 2.


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Figure 3.


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Figure 4.


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Figure 5.


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Figure 6.


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Figure 7.


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Figure 8.


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TOC Image


39

Peptide-Mediated Growth and Dispersion of Au Nanoparticles in Water

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